Spend Analysis, Part 3

As stated in our previous article, it is common for spend data to be extracted from accounting and financial systems. These systems are reliably maintained to provide payment data in currency units. It follows that spend data extracted from accounting systems do inherit a number of accounting terms with it, and these terms do not match Procurement’s view of the supply market. Let us use an example to illustrate the situation.

Consider a number of supplier invoices recorded in the accounting system, as follows:

There are clearly four suppliers for a range of commodities and services supplied and delivered to the organisation. Upon receiving the invoices, records are created in the accounting system with a set of accounting codes that facilitate the book-keeping activity. The accounting codes represent the financial practices and management organisation views of the organisation. More than one set of codes are typically assigned to each invoice: a code that represent the budget holder (or department/cost centre) and another code (from the chart of accounts) to represent the type of cost as an asset, liability, revenue or expense.

Taking a simplified view of the account assignment only (without the organisation view), the aggregated invoice records may look something like this:

From a financial management point of view, Facility services and Repair & Maintenance are accounted as expenses in the Profit & Loss Statement, while the Facility Improvements are capitalised and accounted as assets in the balance sheet.

Procurement View

A Procurement point of view is about the efficient and effective sourcing of supplies from the supplies market. In the example here:

Suppliers ‘Stanley Security’ and ‘ADT Fire & Security’ are likely to be market competitors and are best grouped together under one category of spend called Safety & Security Services.

‘Greenway Services’ provide landscaping services and operate under different market segments than ‘Stanley Security’ and ‘ADT Fire & Safety’, and should not be considered as competitors in the same market space.

The repair and maintenance of motor vehicles (by supplier ‘Rye Motors’) is distinct from other forms of repair and maintenance services which the organisation is likely to use, for example general building and facility repair, plant maintenance, and event IT equipment repair. So it is best to assign spend from this invoice to a category called Motor Vehicles, and together with other forms of purchases of motor vehicle maintenance and repair services.

Applying the Procurement view to the supplier invoice records, we have:

And here lies a key objective of the spend analysis program: the aggregation of historical data according to supply market views to enable Procurement to source and secure the supplies at the most competitive terms from the open market.

Spend Categorisation

Spend categorisation is the core of a spend analysis program. This step is often regarded as the ‘black box’ of spend analysis, and very often fraught with controversies and suspicions among business users and Procurement alike. As in all business processes, there is nothing sinister nor magical about spend categorisation at all.

In many instances, the spend categorisation step is referred to as “data cleansing”, giving the impression that data is scrubbed and changed into a format recognised and used by Procurement only. In reality, data is not transformed nor changed in any way at all. Data is merely enriched with material categories that reflect the supply market view. By enrichment, it is meant that spend data records are processed by applying pre-defined logics (rules) to assign material categories to the data. Once material categories have been assigned, spend data can be aggregated and reported according to the supply markets view. This process of enriching spend data with category information is no different to the task of assigning accounting codes to supplier invoices in the book-keeping and accounting process.

For the application of logical rules to massive amount of data, an IT enabler is an obvious choice. A modest desktop spreadsheet or database application could suffice in basic spend categorisation of up to twenty to fifty thousand records at a time. Larger scale data will need more robust enterprise-level data solutions. Regardless of the scale of the solution deployed, it is the categorisation logic that needs to be defined and executed properly and consistently.

Let’s say that a well-defined taxonomy of material categories exists (in the next article). The following logical rules are often used to categorise spend records:

By suppliers; will need information and knowledge of the core business of suppliers.

By invoice or transaction description; possibly the least efficient rule as the reading and processing of text data is the least preferred method in data processing.

By accounting codes assigned by the Accounting function; inherits some accounting logic which contradicts the Procurement view as highlighted earlier.

By using key codes and looking up into other relevant databases (internal or external).

A combination of the above rules.

There are no standard rules or best practices in terms of spend categorisation. Each organisation has unique data structures and setups. A clear understanding of the data structure and requirement of the spend analysis program is required to define and implement the categorisation rules. It worthwhile to know that spend categorisation rules will eventually fail when unexpected or imprecise data was encountered. The meaning of the phrase “garbage in, garbage out” cannot be more true when applied to the context of spend categorisation. Hence, the spend analysis process incorporates a feedback loop to improve spend categorisation when inaccuracies were detected at the subsequent steps. It is by the precise and diligent corrections undertaken over a period of time will the result of spend analysis be trusted to be a source of truth for the organisation in terms of controlling expenses.

Supplier Aggregation

Just like materials and commodities, suppliers can be grouped into structured views. This is due to the fact that larger scale and multi-national suppliers typically set up and operate different legal entities across the markets. And the names of these legal entities could bear little resemblance to the global brand names of suppliers, due to local country regulations. Hence, an organisation who is unable to aggregate its spend with key suppliers will face the risk of having their economies of scale eroded by unfavourable contracting terms with the key suppliers in the local markets.

To aggregate supplier spends, the ownership and controlling relationship between suppliers need to be known. The most effective way of understanding the supplier relationships is to use an external database such as those provided by Duns & Bradstreet (D&B). Other databases that provide similar information and more relevant to local practices may be used to substitute or supplement D&B. It should be noted that relying on supplier names to establish their ownership and controlling structure is the least effective means in aggregating supplier spend.

In our next and concluding article on Spend Analysis, we will look into the methods of establishing the taxonomy of material categories and some performance metrics for the program.

Chee Kin has extensive experience in developing and implementing business strategies to transform business practices. In his career, he held responsibilities in the structuring and operation of strategic procurement in a logistics company both on the global and regional levels.

ThunderQuote is the most comprehensive business services portal in Singapore, Australia and ASEAN , where hundreds of thousands of dollars of procurement contracts are sourced every month by major companies like Singapore Press Holdings, National Trade Union Congress and more.